PEPR '26 - DPSynth: From Research to Production—Engineering Differentially Private Synthetic... @UsenixOrg

Uploaded June 2026 | Updated September 2026, 3 weeks ago

DPSynth: From Research to Production—Engineering Differentially Private Synthetic Tabular Data at Scale

Mikhail Pravilov, Google

Differentially Private (DP) synthetic data is a promising solution for enabling data-driven innovation while protecting user privacy. However, transforming cutting-edge research in DP into robust, scalable, and usable production systems presents significant engineering challenges. Our library, DPSynth, is based on state-of-the-art marginal-based mechanisms (McKenna et al., 2022), and builds upon the foundations of PipelineDP and mbi libraries.
This talk will share our experience in building and applying DPSynth in production settings, highlighting the journey of productionalizing these research concepts. We'll discuss how DPSynth is built to scale for massive datasets using technologies like Apache Beam and Apache Spark. We will also cover key engineering aspects such as handling real-world data constraints to ensure synthetic data utility and validity, and designing for usability with reasonable defaults for non-DP experts. The library is slated for open-source release prior to the conference, aiming to foster wider adoption of practical DP synthetic data techniques.
Authors: Ryan McKenna, Peter Kairouz, Alexander Knop, Vadym Doroshenko, Eva Bertels

View the full PEPR '26 program at usenix.org/conference/pepr26/program

PEPR 26 - DPSynth: From Research to Production—Engineering Differentially Private Synthetic...

$NSDI 26 - FRCC: Towards Provably Fair and Robust Congestion Control FRCC: Towards Provably Fair and Robust Congestion Control Anup Agarwal, Carnegie Mellon University; Venkat Arun, University of Texas at Austin; Srinivasan Seshan, Carnegie Mellon University Congestion control algorithms (CCAs) play a critical role in network bandwidth allocation. Recent work (from SIGCOMM 2022) showed that a large class of CCAs, including BBR, Copa, and Reno, starve flows in the presence of network jitter. Starvation occurs because CCAs coordinate fairness by encoding fair rates into congestion signals. For example, Renos throughput scales as 1/√loss rate. Even a small amount of noise in these signals leads to large errors in inferring fair rates. We present FRCC (Fair and Robust Congestion Controller), the first CCA that provably bounds unfairness (avoids starvation) even under network jitter. Our key insight is to encode only the flow count (or equivalently, the fair link fraction) into the congestion signals, and independently estimate the link capacity to calculate the fair rate. In this way, we bound jitters impact on fairness. We implement FRCC in the Linux kernel and evaluate it in a variety of network conditions, including synthetic jitter, heterogeneous RTTs, and multi-bottleneck settings. FRCC closely matches the bounds predicted by our theoretical analysis, and consistently achieves fairness, even when state-of-the-art CCAs exhibit starvation. View the full NSDI 26 program at https://www.usenix.org/conference/nsdi26/technical-sessions$

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